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The program did not please everyone. Some teachers felt that the system marked them down without good reason. But they had no way of checking if the program was fair or faulty: the company that built the software, the SAS Institute, regards its algorithm a trade secret and would not disclose its workings. The law has treated others differently. When Wisconsin police arrested Eric Loomis in for driving a car used in a shooting, he was handed a hefty prison term in part because a computer algorithm known as Compas judged him at high risk of re-offending.

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Loomis challenged the sentence because he was unable to check the program. His argument was rejected by the Wisconsin supreme court. A central goal of the field of artificial intelligence is for machines to be able to learn how to perform tasks and make decisions independently, rather than being explicitly programmed with inflexible rules. There are different ways of achieving this in practice, but some of the most striking recent advances, such as AlphaGo, have used a strategy called reinforcement learning.

Typically the machine will have a goal, such as translating a sentence from English to French and a massive dataset to train on.

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It starts off just making a stab at the task — in the translation example it would start by producing garbled nonsense and comparing its attempts against existing translations. After each iteration of the task it improves and after a vast number of reruns, such programs can match and even exceed the level of human translators. Getting machines to learn less well defined tasks or ones for which no digital datasets exist is a future goal that would require a more general form of intelligence, akin to common sense. The arrival of artificial intelligence has raised concerns over computerised decisions to a new high.

Powerful AIs are proliferating in society, through banks, legal firms and businesses, into the National Health Service and government. It is not their popularity that is problematic; it is whether they are fair and can be held to account. Researchers have documented a long list of AIs that make bad decisions either because of coding mistakes or biases ingrained in the data they trained on.

Bad AIs have flagged the innocent as terrorists, sent sick patients home from hospital, lost people their jobs and car licences, had people kicked off the electoral register, and chased the wrong men for child support bills.

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They have discriminated on the basis of names, addresses, gender and skin colour. Bad intentions are not needed to make bad AI. A company might use an AI to search CVs for good job applicants after training it on information about people who rose to the top of the firm. If the culture at the business is healthy, the AI might well spot promising candidates, but if not, it might suggest people for interview who think nothing of trampling on their colleagues for a promotion. How to make AIs fair, accountable and transparent is now one of the most crucial areas of AI research.

Most AIs are made by private companies who do not let outsiders see how they work. Moreover, many AIs employ such complex neural networks that even their designers cannot explain how they arrive at answers. That may not matter if the AI is recommending the next series of Game of Thrones. Last month, the AI Now Institute at New York University, which researches the social impact of AI, urged public agencies responsible for criminal justice, healthcare, welfare and education, to ban black box AIs because their decisions cannot be explained.

Tech firms know that coming regulations and public pressure may demand AIs that can explain their decisions, but developers want to understand them too. It is not good enough for the AI to simply spit out a diagnosis, he says. The pixels turned out to contain a copyright tag for the horse pictures.